Field of the Invention
This invention relates in general to the field energy consumption, and more particularly to an apparatus and method for occupancy determination and applications thereof.
Description of the Related Art
One problem with resources such as electricity, water, fossil fuels, and their derivatives (e.g., natural gas) is related to supply and demand. That is, production of a resource often is not in synchronization with demand for the resource. In addition, the delivery and transport infrastructure for these resources is limited in that it cannot instantaneously match production levels to provide for constantly fluctuating consumption levels. As anyone who has participated in a rolling blackout will concur, the times are more and more frequent when resource consumers are forced to face the realities of limited resource production.
Another problem with resources such as water and fossil fuels (which are ubiquitously employed to produce electricity) is their limited supply along with the detrimental impacts (e.g., carbon emissions) of their use. Public and political pressure for conservation of resources is prevalent, and the effects of this pressure is experienced across the spectrum of resource providers, resource producers and managers, and resource consumers.
It is no surprise, then, that the electrical power generation and distribution community has been taking proactive measures to protect limited instantaneous supplies of electrical power by 1) imposing demand charges on consumers in addition to their monthly usage charge and 2) providing incentives for conservation in the form of rebates and reduced charges. In prior years, consumers merely paid for the total amount of power that they consumed over a billing period. Today, most energy suppliers are not only charging customers for the total amount of electricity they have consumed over the billing period, but they are additionally imposing time of use charges and charging for peak demand. Peak demand is the greatest amount of energy that a customer uses during a measured period of time, typically on the order of minutes. Time of use charges fluctuate throughout the day to dissuade customers from using energy during peak consumption hours. Moreover, energy suppliers are providing rebate and incentive programs that reward consumers for so called energy efficiency upgrades (e.g., lighting and surrounding environment intelligently controlled, efficient cooling and refrigeration, etc.) in their facilities that result in reductions of both peak consumption, time of use consumption shifting, and overall energy consumption. Similar programs are prevalent in the water production and consumption community as well. It is anticipated that such programs will extend to other limited supply energy sources, such as, but not limited to, natural gas.
Demand reduction and energy efficiency programs may be implemented and administered directly by energy providers (i.e., the utilities themselves) or they may be contracted out to third parties, so called energy services companies (ESCOs). ESCOs directly contract with energy consumers and also contract with the energy providers to, say, reduce the demand of a certain resource in a certain area by a specified percentage, where the reduction may be constrained to a certain period of time (i.e., via a demand response program). Or, the reduction effort may be ongoing (i.e., via an energy efficiency program).
The above scenarios are merely examples of the types of programs that are employed in the art to reduce consumption and foster conservation of limited resources. Regardless of the vehicle that is employed, what is important to both producers and consumers is that they be able to understand and appreciate the effects of demand reduction and efficiency improvements that are performed, say, on individual buildings, groups of dissimilar buildings, or buildings of a similar type. How can a building manager know that the capital outlay made to replace 400 windows will result in savings that allow for return of capital within three years? How does an ESCO validate for a contracting regional transmission operator (e.g., Tennessee Valley Authority) that energy efficiency programs implemented on 1,000 consumers will result in a 15 percent reduction in baseline power consumption?
The answers to the above questions are not straightforward, primarily because, as one skilled in the art will appreciate, several factors both drive and often tend to obscure energy consumption. Weather conditions drive consumption, and their effects are significant. For instance, how can a building's energy consumption in January of one year be compared to its consumption in January of another year when average temperatures in the two month's being compared differ by 25 degrees? Is the difference between the two month's power consumption due to weather, or implementation of an energy efficiency program, or a combination of both?
Fortunately, those in the art have developed complex, but widely accepted, normalization techniques that provide for weather normalization of energy use data so that consumption by a building in two different months can be compared without the confusion associated with how outside temperature affects energy use. These modeling techniques provide for normalization of energy use data for buildings and groups of buildings, and they are accurate for the above purposes when employed for energy use periods typically ranging from years down to days. That is, given sufficient historical energy use (“training”) data, models are developed using these normalization techniques, which are acceptable for estimation of a building's energy consumption as a function of outside temperature. These estimates are then employed by the models to remove weather effects from an energy use profile—be it in the past, present or future—and also to predict energy use as a function of temperature.
The present inventors have observed, however, that another significant factor drives energy consumption, and also substantially complicates energy efficiency evaluations. This factor is sometimes referred to as “occupancy,” because in an office or similar facility (e.g., hospital, school, concert hall, airport, poultry shed, etc.) providing for comfort of human or animal life, the amount of energy consumed on an hourly basis (or a time increment less than one hour) is as much a function of the number of living beings that are present in a facility as it is a function of outside temperature. In facilities where energy use for purposes of production dominates (e.g., aggregate plants, steel mills, data processing facilities, server farms), this factor may be referred to as “resource utilization.”
Often, occupancy and resource utilization are cyclical patterns with exceptions on a daily basis. For example, one skilled in the art will appreciate that schools, as well as most office buildings, are generally occupied on weekdays and are unoccupied on weekends, except for holidays. Schools, in addition, are partially occupied during the summer months. Likewise, hospitals are occupied year round, are partially occupied on weekends (as a function of weekend staff reductions), and are over-occupied during flu season. Shopping malls tend to be occupied when school is not in session and are over occupied around and during holidays. Regarding facilities where resource utilization dominates, one skilled in the art will appreciate that server farms utilize more energy around and during holidays because of increased e-commerce, and production facilities utilize energy as a function of economic conditions.
Consider a school which has undergone substantial energy efficiency improvements, but which has also increased in attendance by, say, 20 percent over the previous year. To judge the efficacy of the improvements, an ESCO or other third party may desire to compare the school's energy consumption in, say, September of the new year with that of September of the previous year, where the previous year's energy consumption data has been employed as training data for energy use modeling purposes. As noted above, acceptable techniques provide for normalizing both sets of energy consumption data to remove the effects of weather. But, as one skilled in the art will appreciate, the effects of occupancy changes in the new year will significantly obscure the resulting comparison because more energy per student is consumed in the new year due to the increase in attendance. To remove the effect that occupancy has on this comparison, present day techniques resort to woefully deficient methods such as scaling, “eyeballing,” and other such means that require analyst intervention and subjective judgment. That is, an analyst may judge that a facility is partially occupied because its energy consumption lies at approximately halfway between its minimum and maximum energy consumption values in the training data set. The present inventors have observed that these techniques are disadvantageous and limiting in situations where occupancy need be determined in near real time on a daily basis, or where occupancy (or resource utilization) is to be estimated for a collocated number of facilities or a group of similar facilities. That fact is that occupancy at an aggregated level is incredibly difficult to determine and predict, not only for comparative purposes, but also for purposes of real time control.
Therefore, what is needed is an apparatus and method for automatically determining occupancy of one or more facilities based solely on outside temperature and energy consumption.
What is also needed is a technique for managing the energy consumption of one or more facilities using determined occupancy, where the occupancy is determined as a function of outside temperature and energy use during previous hours.
What is further needed is a technique for controlling security devices and processes in one or more buildings using determined occupancy, where the occupancy is determined as a function of outside temperature and energy use during previous hours.
What is also needed is a technique for managing the energy consumption of one or more facilities using determined resource utilization, where the resource utilization is determined as a function of outside temperature and energy use during previous hours.
What is additionally needed is an occupancy based market control system, where market control devices and processes utilize determined occupancy of corresponding facilities that is determined as a function of outside temperature and energy use during more recent hours.
What is yet also needed is an occupancy based targeted marketing system, where advertising control devices, displays, messaging, and associated processes utilize determined occupancy of corresponding facilities that is determined as a function of outside temperature and energy use during more recent hours.
What is moreover needed is a technique for managing the energy consumption of one or more substantially similar facilities using determined occupancy, where the occupancy is determined as a function of outside temperature and energy use during previous hours.
What is further needed is a mechanism for prioritizing demand response program events, where the events are prioritized according to determined occupancy or resource utilization, and where such determinations are made on the basis of outside temperature and energy use.
What is additionally needed is an apparatus and method for focused marketing messaging based on estimated building occupancy.
What is yet further needed is a technique for forecasting occupancy of buildings based on their energy consumption patterns.